ueshin commented on code in PR #48770:
URL: https://github.com/apache/spark/pull/48770#discussion_r1831904054
##########
python/pyspark/sql/tests/pandas/test_pandas_udf_scalar.py:
##########
@@ -752,46 +752,86 @@ def check_vectorized_udf_return_scalar(self):
def test_udf_with_variant_input(self):
df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as
string)) v")
- from pyspark.sql.functions import col
- scalar_f = pandas_udf(lambda u: str(u), StringType())
+ scalar_f = pandas_udf(lambda u: u.apply(str), StringType(),
PandasUDFType.SCALAR)
iter_f = pandas_udf(
- lambda it: map(lambda u: str(u), it), StringType(),
PandasUDFType.SCALAR_ITER
+ lambda it: map(lambda u: u.apply(str), it), StringType(),
PandasUDFType.SCALAR_ITER
)
+ expected = [Row(udf="{0}".format(i)) for i in range(10)]
+
for f in [scalar_f, iter_f]:
- with self.assertRaises(AnalysisException) as ae:
- df.select(f(col("v"))).collect()
-
- self.check_error(
- exception=ae.exception,
- errorClass="DATATYPE_MISMATCH.UNSUPPORTED_UDF_INPUT_TYPE",
- messageParameters={
- "sqlExpr": '"<lambda>(v)"',
- "dataType": "VARIANT",
- },
- )
+ result = df.select(f(col("v")).alias("udf")).collect()
+ self.assertEqual(result, expected)
def test_udf_with_variant_output(self):
- # Corresponds to a JSON string of {"a": "b"}.
- returned_variant = VariantVal(bytes([2, 1, 0, 0, 2, 5, 98]), bytes([1,
1, 0, 1, 97]))
- scalar_f = pandas_udf(lambda x: returned_variant, VariantType())
+ scalar_f = pandas_udf(
+ lambda u: u.apply(lambda i: VariantVal(bytes([12, i]), bytes([1,
0, 0]))), VariantType()
+ )
iter_f = pandas_udf(
- lambda it: map(lambda x: returned_variant, it), VariantType(),
PandasUDFType.SCALAR_ITER
+ lambda it: map(lambda u: u.apply(
+ lambda i: VariantVal(bytes([12, i]), bytes([1, 0, 0]))
Review Comment:
What does `VariantVal(bytes([12, i]), bytes([1, 0, 0]))` mean?
How can we properly create `VariantVal` as a user?
##########
python/pyspark/sql/tests/test_udf.py:
##########
@@ -334,68 +334,118 @@ def test_udf_with_filter_function(self):
self.assertEqual(sel.collect(), [Row(key=1, value="1")])
def test_udf_with_variant_input(self):
- df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as
string)) v")
-
- u = udf(lambda u: str(u), StringType())
- with self.assertRaises(AnalysisException) as ae:
- df.select(u(col("v"))).collect()
-
- self.check_error(
- exception=ae.exception,
- errorClass="DATATYPE_MISMATCH.UNSUPPORTED_UDF_INPUT_TYPE",
- messageParameters={"sqlExpr": '"<lambda>(v)"', "dataType":
"VARIANT"},
- )
+ for arrow_enabled in ["false", "true"]:
+ with self.sql_conf({"spark.sql.execution.pythonUDF.arrow.enabled":
arrow_enabled}):
Review Comment:
We don't need this here. There is `PythonUDFArrowTests` sharing tests in
`BaseUDFTestsMixin` with the config enabled.
##########
python/pyspark/sql/tests/test_udf.py:
##########
@@ -334,68 +334,118 @@ def test_udf_with_filter_function(self):
self.assertEqual(sel.collect(), [Row(key=1, value="1")])
def test_udf_with_variant_input(self):
- df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as
string)) v")
-
- u = udf(lambda u: str(u), StringType())
- with self.assertRaises(AnalysisException) as ae:
- df.select(u(col("v"))).collect()
-
- self.check_error(
- exception=ae.exception,
- errorClass="DATATYPE_MISMATCH.UNSUPPORTED_UDF_INPUT_TYPE",
- messageParameters={"sqlExpr": '"<lambda>(v)"', "dataType":
"VARIANT"},
- )
+ for arrow_enabled in ["false", "true"]:
+ with self.sql_conf({"spark.sql.execution.pythonUDF.arrow.enabled":
arrow_enabled}):
+ df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as
string)) v")
+ u = udf(lambda u: str(u), StringType())
+ expected = [Row(udf="{0}".format(i)) for i in range(10)]
+ result = df.select(u(col("v")).alias("udf")).collect()
+ self.assertEqual(result, expected)
def test_udf_with_complex_variant_input(self):
- df = self.spark.range(0, 10).selectExpr(
- "named_struct('v', parse_json(cast(id as string))) struct_of_v"
- )
-
- u = udf(lambda u: str(u), StringType())
-
- with self.assertRaises(AnalysisException) as ae:
- df.select(u(col("struct_of_v"))).collect()
-
- self.check_error(
- exception=ae.exception,
- errorClass="DATATYPE_MISMATCH.UNSUPPORTED_UDF_INPUT_TYPE",
- messageParameters={
- "sqlExpr": '"<lambda>(struct_of_v)"',
- "dataType": "STRUCT<v: VARIANT NOT NULL>",
- },
- )
+ for arrow_enabled in ["false", "true"]:
+ with self.sql_conf({"spark.sql.execution.pythonUDF.arrow.enabled":
arrow_enabled}):
Review Comment:
ditto, and following tests in `BaseUDFTestsMixin`.
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